Abstract
This paper presents iMPAcT tool that tests recurring common behavior on Android mobile applications. The process followed combines exploration, reverse engineering and testing to automatically test Android mobile applications. The tool explores automatically the App by firing UI events. After each event fired, the tool checks if there are UI patterns present using a reverse engineering process. If a UI pattern is present, the tool runs the corresponding testing strategy (Test Pattern). During reverse engineering the tool uses a catalog of UI Patterns which describes recurring behavior (UI Patterns) to test and the corresponding test strategies (Test Patterns). This catalog may be extended in the future as needed (e.g., to deal with new interaction trends). This paper describes the implementation details of the iMPAcT tool, the catalog of patterns used, the outputs produced by the tool and the results of experiments performed in order to evaluate the overall testing approach. These results show that the overall testing approach is capable of finding failures on existing Android mobile applications.

Abstract
Frequently software testing tends to be neglected at the beginning of the projects, only performed on the late stage. However, it is possible to benefit from combining requirement with testing specification activities. On one hand, acceptance tests specification will require less manual effort since they are defined or generated automatically from the requirements specification. On the other hand, the requirements specification itself will end up having higher quality due to the use of a more structured language, reducing typical problems such as ambiguity, inconsistency and incorrectness. This research proposes an approach that promotes the practice of tests specification since the very beginning of projects, and its integration with the requirements specification itself. It is a model-driven approach that contributes to maintain the requirements and tests alignment, namely between requirements, test cases, and low-level automated test scripts. To show the applicability of the approach, two complementary languages are adopted: the ITLingo RSL that is particularly designed to support both requirements and tests specification; and the Robot language, which is a low-level keyword-based language for the specification of test scripts. The approach includes model-to-model transformation techniques, such as test cases into test scripts transformations. In addition, these test scripts are executed by the Robot test automation framework.

Abstract
Smartphones are becoming more important in our everyday lives and it is increasingly common to perform critical tasks on these devices, such as making payments. For this reason, ensuring the quality of these applications is an important task. One way to do this is through software testing. However, the testing of these applications presents major challenges due to the wide variety of devices available in the market. In this context, automated testing gains more relevance. There are dynamic test approaches for testing mobile applications, but there are some challenges that need to be overcome for good results, such as, being able to explore the complete behaviour of the application (e.g., overcoming blocking points); choosing appropriate input data; testing dynamic behaviour; testing specific characteristics of mobile applications, such as specific forms of interaction, e.g., long press, and so on. This paper presents a dynamic exploration approach of Android mobile applications that aims to overcome some of the problems identified. During the exploration process, the algorithm builds a Finite State Machine where states are traversed screens and transitions between states describe events that allow moving from one screen to another. This approach is implemented as an extension of the iMPAcT tool. The approach is validated over real Google Play apps and the test coverage results achieved are presented, compared and discussed. © Springer Nature Switzerland AG 2019.

Abstract
Regression testing is of paramount importance to ensure that the quality of software does not suffer when code changes are implemented. However, having a large set of tests is mostly done by hand and is time-consuming. Regression tests are written to test functionality that is already implemented and thus are a prime target for automatic test generation. Mutation testing is a technique that evaluates the quality of tests by applying simple changes to source code and checking if any test detects those changes. This paper presents an approach focused on GUI Testing that takes the idea behind mutation testing and applies it, not to the source code, but the actual tests. Generated tests are then analyzed, and those that generate different outcomes are chosen. The set of initial test cases is obtained from the interactions of the actual users of the service under analysis. In the end, an evaluation of the approach is presented. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
Introduction. Online health forums help to surface and organize patients' knowledge and make it useful for many. They are used by many to seek for advice or to share what they know about health subjects. Being an important communication medium, it's important to understand why and how it is used. Method. In this work we examine and categorize messages of an online health forum, with the purpose of providing a classification scheme that can be used by the research community in future analyses. The definition of the classification scheme was iterative and its inter-rater reliability was assessed twice using Cohen's Kappa statistic. Analysis. The classification scheme arose from a content analysis of 3,399 messages from several communities of an online health forum. Findings. The scheme is divided into four sections of categories and each section has several subcategories, in total there are 23 subcategories. The inter-rater agreement assessment of the scheme showed a good consistency between coders. The majority of the categories has a Cohen's Kappa agreement above 0.4. Conclusion. The proposed classification scheme facilitates the analysis of messages exchanged in online health forums for several purposes, including studies of information seeking.